Data warehouse risk management – risk identification

Data warehouse projects are highly complex, and as such, are inherently risky. It is the responsibility of the project manager to lead the data warehouse team in identifying all risks associated with a particular data warehouse implementation. The goal of this process is to document all essential information relating to project risk.

For additional information relating to risk, refer to the Project Diva article on project risk identification and management.

 

Risk ID

 

Category

 

Description

 

Conditions

 

Impact Description

 

Possible mitigation

 

1

 

Overall

 

Loss of project sponsor

 

Impending reorganization

 

Scope, budget, staffing, schedule issues

 

Identify secondary sponsor. Review project requirements with
sponsors

 

2

 

Overall

 

General lack of experience with toolsets, methods and best
practices

 

Potential for huge margins of error in budgeting and scheduling;
project delays; deliverables not fit for use

 

Strong resource management plan; clearly defined project
responsibilities; recruiting staff with DW experience; off-site training;
professional consulting

 

3

 

Scope

 

Wide range of users driving system requirements

 

Conflicting user requirements

 

Scope creep; application not meet user requirements; marginalized
users

 

Ensure high level of user involvement; requirement
prioritization

 

4

 

Scope

 

Changing system requirements

 

Impending reorganization; new product development; staff
turnover

 

Schedule delays; system not meet business
objectives

 

Change management process; project sponsor
involvement

 

5

 

Budget

 

Inadequate Budget

 

Project delay; scope scaled back; not meet business
requirements

 

Research; professional cost estimation; contingency budgeting; sponsor
support

 

6

 

Schedule

 

Unrealistic schedule due to initial estimates / poorly understood
deliverables

 

Large initial delays; evidence of tasks excluded from
WBS

 

Project delay; quality issues due to rushed delivery or exclusion of
deliverables

 

Research; sponsor support; professional
consulting

 

7

 

ROI

 

Disconnect between business objectives and project
deliverables

 

Changing business objectives; change in management; market
changes

 

Product not fit for use; no ROI

 

Proactive alignment of project with business objectives; active
stakeholder management; prototyping

 

8

 

Technical

 

Scalability issues due to huge amounts of data, changing
requirements

 

Poor system performance

 

Data access issues

 

Estimating toolsets; technical design completed by experienced
DBA

 

9

 

Technical

 

Support issues; heterogeneous environment; new
technologies

 

Staff turnover; staff currently not trained on
systems

 

System unavailable

 

Administrator training; staff training budget; DR
plan

 

10

 

Technical

 

Poor data quality

 

Useless datasets; not meet business requirements; system not
used

 

Data quality review sub project; data cleansing; implement known clean
data first; metadata quality tags (e.g. “questionable,” “not yet
reviewed”)

 

11

 

Technical

 

End user technical skills too low

 

Implement canned reports and templates; user involvement in front end
design

 

12

 

Implementation

 

Vendor issues

 

Budget; project delays

 

Verify vendor financial viability; vendor
references

 

13

 

Implementation

 

User rejection

 

System not used; users express dissatisfaction during training,
deployment

 

High level of user involvement; prototyping; user feedback incorporated
into project; user training

 

Data Warehouse Risk Identification (.doc format)

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